Spatially resolved immune microenvironmental profiling for follicular thyroid carcinoma with minimal capsular invasion

Prognostic Analysis Combining Histopathological Features and Clinical Information to Predict Colorectal Cancer Survival from Whole-Slide Images

BACKGROUND

Colorectal cancer (CRC) is a malignant tumor within the digestive tract with both a high incidence rate and mortality. Early detection and intervention could improve patient clinical outcomes and survival.

METHODS

This study computationally investigates a set of prognostic tissue and cell features from diagnostic tissue slides. With the combination of clinical prognostic variables, the pathological image features could predict the prognosis in CRC patients. Our CRC prognosis prediction pipeline sequentially consisted of three modules: (1) A MultiTissue Net to delineate outlines of different tissue types within the WSI of CRC for further ROI selection by pathologists. (2) Development of three-level quantitative image metrics related to tissue compositions, cell shape, and hidden features from a deep network. (3) Fusion of multi-level features to build a prognostic CRC model for predicting survival for CRC.

RESULTS

Experimental results suggest that each group of features has a particular relationship with the prognosis of patients in the independent test set. In the fusion features combination experiment, the accuracy rate of predicting patients' prognosis and survival status is 81.52%, and the AUC value is 0.77.

CONCLUSION

This paper constructs a model that can predict the postoperative survival of patients by using image features and clinical information. Some features were found to be associated with the prognosis and survival of patients.

Macrophage barrier in the tumor microenvironment and potential clinical applications

The tumor microenvironment (TME) constitutes a complex microenvironment comprising a diverse array of immune cells and stromal components. Within this intricate context, tumor-associated macrophages (TAMs) exhibit notable spatial heterogeneity. This heterogeneity contributes to various facets of tumor behavior, including immune response modulation, angiogenesis, tissue remodeling, and metastatic potential. This review summarizes the spatial distribution of macrophages in both the physiological environment and the TME. Moreover, this paper explores the intricate interactions between TAMs and diverse immune cell populations (T cells, dendritic cells, neutrophils, natural killer cells, and other immune cells) within the TME. These bidirectional exchanges form a complex network of immune interactions that influence tumor immune surveillance and evasion strategies. Investigating TAM heterogeneity and its intricate interactions with different immune cell populations offers potential avenues for therapeutic interventions. Additionally, this paper discusses therapeutic strategies targeting macrophages, aiming to uncover novel approaches for immunotherapy. Video Abstract.

New Insights into Immune Cells and Immunotherapy for Thyroid Cancer

Thyroid cancer (TC) is the most common endocrine malignancy worldwide, and the incidence of TC has gradually increased in recent decades. Differentiated thyroid cancer (DTC) is the most common subtype and has a good prognosis. However, advanced DTC patients with recurrence, metastasis and iodine refractoriness, as well as more aggressive subtypes such as poorly differentiated thyroid cancer (PDTC) and anaplastic thyroid cancer (ATC), still pose a great challenge for clinical management. Therefore, it is necessary to continue to explore the inherent molecular heterogeneity of different TC subtypes and the global landscape of the tumor immune microenvironment (TIME) to find new potential therapeutic targets. Immunotherapy is a promising therapeutic strategy that can be used alone or in combination with drugs targeting tumor-driven genes. This article focuses on the genomic characteristics, tumor-associated immune cell infiltration and immune checkpoint expression of different subtypes of TC patients to provide guidance for immunotherapy.

Automated diagnosis and management of follicular thyroid nodules based on the devised small-dataset interpretable foreground optimization network deep learning: a multicenter diagnostic study

BACKGROUND

Currently, follicular thyroid carcinoma (FTC) has a relatively low incidence with a lack of effective preoperative diagnostic means. To reduce the need for invasive diagnostic procedures and to address information deficiencies inherent in a small dataset, we utilized interpretable foreground optimization network deep learning to develop a reliable preoperative FTC detection system.

METHODS

In this study, a deep learning model (FThyNet) was established using preoperative ultrasound images. Data on patients in the training and internal validation cohort ( n =432) were obtained from Ruijin Hospital, China. Data on patients in the external validation cohort ( n =71) were obtained from four other clinical centers. We evaluated the predictive performance of FThyNet and its ability to generalize across multiple external centers and compared the results yielded with assessments from physicians directly predicting FTC outcomes. In addition, the influence of texture information around the nodule edge on the prediction results was evaluated.

RESULTS

FThyNet had a consistently high accuracy in predicting FTC with an area under the receiver operating characteristic curve (AUC) of 89.0% [95% CI 87.0-90.9]. Particularly, the AUC for grossly invasive FTC reached 90.3%, which was significantly higher than that of the radiologists (56.1% [95% CI 51.8-60.3]). The parametric visualization study found that those nodules with blurred edges and relatively distorted surrounding textures were more likely to have FTC. Furthermore, edge texture information played an important role in FTC prediction with an AUC of 68.3% [95% CI 61.5-75.5], and highly invasive malignancies had the highest texture complexity.

CONCLUSION

FThyNet could effectively predict FTC, provide explanations consistent with pathological knowledge, and improve clinical understanding of the disease.

Crosstalk between Thyroid Carcinoma and Tumor-Correlated Immune Cells in the Tumor Microenvironment

Thyroid cancer (TC) is the most common malignancy in the endocrine system. Although most TC can achieve a desirable prognosis, some refractory thyroid carcinomas, including radioiodine-refractory differentiated thyroid cancer, as well as anaplastic thyroid carcinoma, face a myriad of difficulties in clinical treatment. These types of tumors contribute to the majority of TC deaths due to limited initial therapy, recurrence, and metastasis of the tumor and tumor resistance to current clinically targeted drugs, which ultimately lead to treatment failure. At present, a growing number of studies have demonstrated crosstalk between TC and tumor-associated immune cells, which affects tumor deterioration and metastasis through distinct signal transduction or receptor activation. Current immunotherapy focuses primarily on cutting off the interaction between tumor cells and immune cells. Since the advent of immunotherapy, scholars have discovered targets for TC immunotherapy, which also provides new strategies for TC treatment. This review methodically and intensively summarizes the current understanding and mechanism of the crosstalk between distinct types of TC and immune cells, as well as potential immunotherapy strategies and clinical research results in the area of the tumor immune microenvironment. We aim to explore the current research advances to formulate better individualized treatment strategies for TC patients and to provide clues and references for the study of potential immune checkpoints and the development of immunotherapy technologies.